The results for the segmentation of the pancreas show promise, despite its simple nature of employing deep ConvNets for image superpixel classification. Different scales and Gaussian smoothing strategies are addressed and evaluated. We performed similar or better to the recent state-of-the art work of pancreas segmentation that reports average Dice scores ranging from 46.6% to 68.8% [1]}, [2]}, [3]}. Other abdomen organs are jointly segmented in a multiple atlas fusion framework [1]}, [2]} to boost each other’s segmentation accuracy whereas we formulate the pancreas segmentation task as a standalone foreground/background separation problem. Note that [1]}, [2]} use leave-one-patient-out cross validation which is relatively computation expensive and may not scale up efficiently a large patient population. In particular, [2]} reports 68% Dice coefficient with 150 patients and it drops significantly to 58% when having only 50 patients under leave-one-out testing. Our results are based on a random 60 /2 /20 training and testing split.
